Environmental Engineering and Management, SERD, Asian Institute of Technology, Pathumthani 12120, Thailand.
Sci Total Environ. 2011 May 1;409(11):2261-71. doi: 10.1016/j.scitotenv.2011.02.022.
This study investigated the main causes of haze episodes in the northwestern Thailand to provide early warning and prediction. In an absence of emission input data required for chemical transport modeling to predict the haze, the climatological approach in combination with statistical analysis was used. An automatic meteorological classification scheme was developed using regional meteorological station data of 8years (2001-2008) which classified the prevailing synoptic patterns over Northern Thailand into 4 patterns. Pattern 2, occurring with high frequency in March, was found to associate with the highest levels of 24h PM(10) in Chiangmai, the largest city in Northern Thailand. Typical features of this pattern were the dominance of thermal lows over India, Western China and Northern Thailand with hot, dry and stagnant air in Northern Thailand. March 2007, the month with the most severe haze episode in Chiangmai, was found to have a high frequency of occurrence of pattern 2 coupled with the highest emission intensities from biomass open burning. Backward trajectories showed that, on haze episode days, air masses passed over the region of dense biomass fire hotspots before arriving at Chiangmai. A stepwise regression model was developed to predict 24h PM(10) for days of meteorology pattern 2 using February-April data of 2007-2009 and tested with 2004-2010 data. The model performed satisfactorily for the model development dataset (R(2)=87%) and test dataset (R(2)=81%), which appeared to be superior over a simple persistence regression of 24h PM(10) (R(2)=76%). Our developed model had an accuracy over 90% for the categorical forecast of PM(10)>120μg/m(3). The episode warning procedure would identify synoptic pattern 2 and predict 24h PM(10) in Chiangmai 24h in advance. This approach would be applicable for air pollution episode management in other areas with complex terrain where similar conditions exist.
本研究旨在探讨泰国西北部霾事件的主要成因，以期提供预警和预测。由于缺乏化学输送模型预测霾所需的排放输入数据，本研究采用气候学方法结合统计分析。利用 8 年（2001-2008 年）区域气象站数据开发了一种自动气象分类方案，将泰国北部的主要天气模式分为 4 种模式。模式 2 在 3 月出现频率较高，与泰国北部最大城市清迈的 24 小时 PM10 水平最高有关。该模式的典型特征是印度、中国西部和泰国北部上空热低压占主导地位，泰国北部地区空气炎热、干燥且停滞。2007 年 3 月是清迈发生最严重霾事件的月份，发现该月模式 2 出现频率高，同时生物质露天燃烧排放强度也最高。后向轨迹显示，在霾事件日，空气团在到达清迈之前经过了密集生物质火热点地区。利用 2007-2009 年 2-4 月数据建立了模式 2 日 24 小时 PM10 的逐步回归预测模型，并利用 2004-2010 年数据进行了检验。该模型在模型开发数据集（R2=87%）和测试数据集（R2=81%）中的表现令人满意，明显优于简单的 24 小时 PM10 持续回归（R2=76%）。对于 PM10>120μg/m3 的分类预报，我们开发的模型准确率超过 90%。该事件预警程序将识别天气模式 2，并提前 24 小时预测清迈的 24 小时 PM10。该方法适用于其他地形复杂、存在类似条件的地区的空气污染事件管理。
